Additionally, it is possible to model the variance intrinsic to the learned model, that is how certain of a given prediction the network is.
Epistemic uncertainty can, in principle, be reduced if more data is used for training.
In our case, it accounts for the network's poor predictions on out-of-data samples such as scene features that were rarely encountered during training.
Epistemic uncertainty can be modeled by placing a distribution over the network's weights.
Unfortunately, given a prior distribution \(\mathcal {N}_{{\mathbf {W}}}(0, \sigma )\)  over the weights, it is computationally difficult to analytically infer the posterior \(p({\mathbf {W}}| {\mathbf {X}}, {\mathbf {Y}})\) , that is the probability of drawing a set of weights given the network input and output.
There is a number of proposed methods to approximate this posterior [1]}, [2]}.
The work in [3]} produces an approximation using dropout variational inference.
This has proven to be an effective yet easily obtainable Bayesian approximation when dealing with deep complex models.
